H21I:
Hydrologic Data Assimilation II Posters

Tuesday, 16 December 2014: 8:00 AM-12:20 PM
Chairs:  Dongryeol Ryu, The University of Melbourne, Parkville, Australia and Valentijn R N Pauwels, Monash University, Melbourne, Australia
Primary Conveners:  Barton A Forman, University of Maryland, College Park, MD, United States
Co-conveners:  Valentijn R N Pauwels, Monash University, Melbourne, Australia, Dongryeol Ryu, The University of Melbourne, Parkville, Australia and M Tugrul Yilmaz, Middle East Technical University, Ankara, Turkey
OSPA Liaisons:  M Tugrul Yilmaz, Middle East Technical University, Civil Engineering, Ankara, Turkey

Abstracts Submitted to this Session:

 
Deconvolution of Soil Moisture and Vegetation Emissions from Passive Microwave Brightness Temperatures Using Visible/Infrared Observations
David Truesdale, Jeffrey H Bowles, Li Li, Bo-Cai Gao and Gia Lamela, Naval Research Lab DC, Remote Sensing, Washington, DC, United States
 
Assimilation of Multiscale and Multivariable Hydrologic Variables over Pan European River Basins
Oldrich Rakovec1, Rohini Kumar1, Luis E Samaniego1, Juliane Mai1, Stephan Thober2 and Matthias Cuntz1, (1)Helmholtz Centre for Environmental Research UFZ Leipzig, Leipzig, Germany, (2)Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany
 
Impact of Rescaling Anomaly and Seasonal Components of Soil Moisture on Hydrologic Data Assimilation
M Tugrul Yilmaz, Middle East Technical University, Ankara, Turkey, Wade T Crow, Hydrol and Remote Sensing Lab, Beltsville, MD, United States and Dongryeol Ryu, The University of Melbourne, Parkville, Australia
 
Characterizing Precipitation Forcing Uncertainty in Land Data Assimilation Using an Ensemble-Based Bayesian Approach
Seyed Hamed Alemohammad, Dennis McLaughlin and Dara Entekhabi, Massachusetts Institute of Technology, Civil and Environmental Engineering, Cambridge, MA, United States
 
Assimilation of satellite observed brightness temperature and terrestrial water storage into the Catchment land surface model for improved soil moisture estimation
Manuela Girotto1,2, Gabrielle J.M. De Lannoy1,2, Rolf H Reichle1 and Matthew Rodell1, (1)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (2)Universities Space Research Association Columbia, Columbia, MD, United States
 
The SMAP Level 4 Surface and Root Zone Soil Moisture data assimilation product
Rolf H Reichle, NASA GSFC, Greenbelt, MD, United States, Gabrielle J.M. De Lannoy, NASA Goddard Space Flight Center, Greenbelt, MD, United States, Wade T Crow, Hydrol and Remote Sensing Lab, Beltsville, MD, United States, John S Kimball, The University of Montana, Flathead Lake Biological Station, Polson, MT, United States, Randal D Koster, NASA Goddard SFC, Greenbelt, MD, United States and Qing Liu, GMAO, building 33, Greenbelt, MD, United States
 
Extended Triple Collocation: Estimating Errors And Correlation Coefficients With Respect To An Unknown Target
Kaighin A Mccoll1, Jur Vogelzang2, Alexandra G Konings3, Dara Entekhabi3, Maria Piles4 and Ad Stoffelen2, (1)MIT, Cambridge, MA, United States, (2)Royal Netherlands Meteorological Institute, De Bilt, Netherlands, (3)Massachusetts Institute of Technology, Civil and Environmental Engineering, Cambridge, MA, United States, (4)SMOS Barcelona Expert Center, Barcelona, Spain
 
Are Dynamically Evolving Models the Future of Hydrologic Modelling? A Data Assimilation Approach
Sahani Darshika Pathiraja1, Lucy Amanda Marshall1, Ashish Sharma1 and Hamid Moradkhani2, (1)University of New South Wales, School of Civil and Environmental Engineering, Sydney, NSW, Australia, (2)Portland State University, Civil and Environmental Engineering, Portland, OR, United States
 
“Wishful thinking” – how much rainfall information can be inferred from soil moisture and runoff?
Ming Pan1, Wang Zhan1, Niko Wanders2 and Eric F Wood1, (1)Princeton University, Princeton, NJ, United States, (2)Utrecht University, Utrecht, 3584, Netherlands
 
Streamflow Data Assimilation in SWAT Model Using Extended Kalman Filter
Leqiang Sun, Ioan Nistor and Ousmane Seidou, University of Ottawa, Department of Civil Engineering, Ottawa, ON, Canada
 
A Study on Estimation of Quantile Using Regional Scaling Factors
Younghun Jung, Sunghun Kim, Hanjin Jang and Jun-Haeng Heo, Yonsei University, Seoul, South Korea
 
Translating above-ground cosmic-ray neutron intensity to high-frequency soil moisture profile at sub-kilometer scale
Rafael Rosolem1, Timothy J Hoar2, Avelino F Arellano3, Jeffrey L Anderson4, William J Shuttleworth3, Xubin Zeng3 and Trenton E Franz5, (1)University of Bristol, Bristol, BS8, United Kingdom, (2)Natl Ctr Atmospheric Res, Boulder, CO, United States, (3)University of Arizona, Tucson, AZ, United States, (4)University Corporation for Atmospheric Research, Boulder, CO, United States, (5)University of Nebraska Lincoln, Lincoln, NE, United States
 
Multi-scale analysis framework for bias characterisation, bias correction and de-noising
Chun-Hsu Su, University of Melbourne, Parkville, VIC, Australia and Dongryeol Ryu, The University of Melbourne, Parkville, Australia
 
Improvement of NCEP Numerical Weather Prediction with Use of Satellite Land Measurements
Weizhong Zheng1,2, Michael B Ek1, Helin Wei1,2, Jesse Meng1,2, Jiarui Dong1,2, Yihua Wu1,2, Xiwu Zhan3, Jicheng Liu3, Zhangyan Jiang2,3 and Marco Vargas3, (1)NOAA/NWS/NCEP, College Park, MD, United States, (2)IMSG, College Park, MD, United States, (3)NOAA-NESDIS, College Park, MD, United States
 
Fusion of multiple radar-based quantitative precipitation estimates (QPE) for high-resolution flash flood forecasting in large urban areas
Arezoo Rafieei Nasab, Amir Norouzi, Beomgeun Kim and Dong-Jun Seo, Univ of TX-Arlington-Civil Eng, Arlington, TX, United States
 
Robust Estimator for Annual Rainfall Erosivity in Korea
Joon-Hak Lee1, Hyun-Keun Song2, Hongjoon Shin2 and Jun-Haeng Heo2, (1)Korea Military Academy, Seoul, South Korea, (2)Yonsei University, Seoul, South Korea
 
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